His primary areas of study are Remote sensing, Terrain, Lidar, Canopy and Change detection. He interconnects Terrain analysis, Digital soil mapping, Tree, Physical geography and Vegetation in the investigation of issues within Remote sensing. His work carried out in the field of Vegetation brings together such families of science as Remote sensing and Active fire.
Paul E. Gessler has included themes like Soil science and Soil horizon in his Terrain study. In his study, which falls under the umbrella issue of Soil horizon, Hydrology is strongly linked to Pedogenesis. He studied Lidar and Basal area that intersect with Sustainable forest management, Habitat, Random forest, Forest management and Ecological succession.
His scientific interests lie mostly in Remote sensing, Lidar, Hydrology, Vegetation and Canopy. His work in the fields of Remote sensing, such as Advanced Spaceborne Thermal Emission and Reflection Radiometer, intersects with other areas such as Empirical modelling. His study in Lidar is interdisciplinary in nature, drawing from both Terrain, Forest management, Forestry, Basal area and Tree.
The Hydrology study combines topics in areas such as Landslide, Soil science, Soil water and Geographic information system. His Normalized Difference Vegetation Index and Fire regime study, which is part of a larger body of work in Vegetation, is frequently linked to Atlas data and Fire behavior, bridging the gap between disciplines. His research in Canopy intersects with topics in Biomass, Atmospheric sciences, Bulk density, Range and Meteorology.
Paul E. Gessler focuses on Hydrology, Spillover effect, Lassa virus, Wildlife and Ecology. His biological study spans a wide range of topics, including Perennial plant, Soil water and Geospatial analysis. There are a combination of areas like Transmission, Development economics, Conventional wisdom, Culling and Natural resource economics integrated together with his Spillover effect study.
His Lassa virus study spans across into fields like Sierra leone and Lassa fever.
His main research concerns Remote sensing, Ancillary data, Thematic Mapper, Land cover and Context. He is interested in Change detection, which is a field of Remote sensing. His Ancillary data study frequently draws connections between related disciplines such as Land use, land-use change and forestry.
His Context study overlaps with Overfitting, Random forest, Artificial neural network and Ensemble learning.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Soil Attribute Prediction Using Terrain Analysis
I.D. Moore;P.E. Gessler;G.A. Nielsen;G.A. Peterson.
Soil Science Society of America Journal (1993)
Remote sensing techniques to assess active fire characteristics and post-fire effects
Leigh B. Lentile;Zachary A. Holden;Alistair M. S. Smith;Michael J. Falkowski.
International Journal of Wildland Fire (2006)
Soil-landscape modelling and spatial prediction of soil attributes
Paul E. Gessler;I. D. Moore;N. J. McKenzie;P. J. Ryan.
International Journal of Geographic Information Systems (1995)
Modeling Soil–Landscape and Ecosystem Properties Using Terrain Attributes
P. E. Gessler;O. A. Chadwick;F. Chamran;L. Althouse.
Soil Science Society of America Journal (2000)
Splines — more than just a smooth interpolator
M.F. Hutchinson;P.E. Gessler.
Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA
Marwa Waseem A. Halmy;Paul E. Gessler;Jeffrey A. Hicke;Boshra B. Salem.
Applied Geography (2015)
Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA
Michael J. Falkowski;Michael J. Falkowski;Jeffrey S. Evans;Sebastian Martinuzzi;Paul E. Gessler.
Remote Sensing of Environment (2009)
Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data
Michael J Falkowski;Alistair M.S Smith;Andrew T Hudak;Paul E Gessler.
Canadian Journal of Remote Sensing (2006)
Spatial Prediction of Landslide Hazard Using Logistic Regression and ROC Analysis
Pece V Gorsevski;Paul E Gessler;Randy B Foltz;William J Elliot.
Transactions in Gis (2006)
Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments
Steven E. Sesnie;Steven E. Sesnie;Paul E. Gessler;Bryan Finegan;Sirpa Thessler.
Remote Sensing of Environment (2008)
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